Cohort Representation

Cohort representation in research focuses on effectively leveraging groups of individuals with shared characteristics to improve the power and generalizability of analyses, particularly in machine learning applications across diverse fields like healthcare and fraud detection. Current research emphasizes developing methods for automated cohort identification and representation, often employing graph-based approaches, attention mechanisms, and deep learning models to capture complex relationships within and between cohorts. This work is crucial for addressing data heterogeneity, improving model performance, and generating more robust and interpretable insights, ultimately leading to more effective clinical trials, diagnostic tools, and predictive models.